In this paper we have made a link between the classical Hopfield model for neural networks of associative memory, first studied by [1]} and [2]}, where both the neuron activity and the memory patterns are represented by Ising spins, and the purely Gaussian model, studied by [3]}, where the memories are made of Gaussian distributed variables. The former has the best properties concerning retrieval, while the latter totally lacks the phase in which such states are stable and it cannot serve as a model of associative memory if not for zero storage capacity \(\alpha \) . By considering a mixed model, where the memories contain both bimodal and Gaussian variables, we studied the thermodynamics and we did a numerical analysis of the attractors landscape, as the relative fractions of the two kinds of variables change.
